The iris data set: In search of the source of virginica

Significance ◽  
2021 ◽  
Vol 18 (6) ◽  
pp. 26-29
Author(s):  
Antony Unwin ◽  
Kim Kleinman
Keyword(s):  
Data Set ◽  
Iris Data

Gaussian Based Classification with Application to the Iris Data Set**

2011 ◽  
Vol 44 (1) ◽  
pp. 14271-14276 ◽  
Author(s):  
H. Chang ◽  
A. Astolfi
Keyword(s):  
Data Set ◽  
Iris Data

scholarly journals Membership Functions for Fuzzy Focal Elements

2016 ◽  
Vol 26 (3) ◽  
pp. 395-427 ◽  
Author(s):  
Sebastian Porębski ◽  
Ewa Straszecka
Keyword(s):  
Membership Function ◽  
Data Driven ◽  
Membership Functions ◽  
Data Set ◽  
Dempster Shafer Theory ◽  
Elimination Algorithm ◽  
Rule Sets ◽  
Shafer Theory ◽  
Iris Data

Abstract The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated.

scholarly journals Comments on the CASIA version 1.0 Iris Data Set

2007 ◽  
Vol 29 (10) ◽  
pp. 1869-1870 ◽  
Author(s):  
P. Jonathon Phillips ◽  
Kevin W. Bowyer ◽  
Patrick J. Flynn
Keyword(s):  
Data Set ◽  
Iris Data

EXTRACTING RULES FROM TRAINED RBF NEURAL NETWORKS

2005 ◽  
Vol 1 ◽  
pp. 33
Author(s):  
Peter Grabusts
Keyword(s):  
Neural Networks ◽  
Rbf Neural Network ◽  
Extraction Procedure ◽  
Rule Extraction ◽  
Data Sets ◽  
Rbf Neural Networks ◽  
Data Set ◽  
Extraction Algorithm ◽  
Rule Set ◽  
Iris Data

This paper describes a method of rule extraction from trained artificial neural networks. The statement of the problem is given. The aim of rule extraction procedure and suitable neural networks for rule extraction are outlined. The RULEX rule extraction algorithm is discussed that is based on the radial basis function (RBF) neural network. The extracted rules can help discover and analyze the rule set hidden in data sets. The paper contains an implementation example, which is shown through standalone IRIS data set.

A PROPOSED MAX-PRODUCT THRESHOLD UNIT FOR CLASSIFICATION OF PATTERN VECTORS

2001 ◽  
Vol 11 (03) ◽  
pp. 271-279 ◽  
Author(s):  
ROELOF K BROUWER
Keyword(s):  
Weight Vector ◽  
Data Set ◽  
Benchmark Data ◽  
Cervical Cell ◽  
Credit Data ◽  
Cell Data ◽  
Pattern Vector ◽  
Iris Data

This paper proposes a max-product threshold unit (maptu) that can successfully perform dichotomous classifications of pattern vectors. Maptu, with weight vector, w, classifies a pattern vector, x, by comparing x max-prod w to 0.5. Results obtained by other methods in classification of benchmark data are used for comparison to the method using maptu. The benchmark data consists of the Australian credit data set, cervical cell data set, diabetes data set and the iris data set.

A probabilistic modeling approach for interpretable data inference and classification

2020 ◽  
pp. 1-17
Author(s):  
Shuaiyu Yao ◽  
Jian-Bo Yang ◽  
Dong-Ling Xu
Keyword(s):  
Statistical Analysis ◽  
Probabilistic Modeling ◽  
Data Set ◽  
Inference Mechanism ◽  
Modeling Approach ◽  
Input Variables ◽  
Data Inference ◽  
The Relationship ◽  
Better Than ◽  
Iris Data

In this paper, we propose a new probabilistic modeling approach for interpretable inference and classification using the maximum likelihood evidential reasoning (MAKER) framework. This approach integrates statistical analysis, hybrid evidence combination and belief rule-based (BRB) inference, and machine learning. Statistical analysis is used to acquire evidence from data. The BRB inference is applied to analyze the relationship between system inputs and outputs. An interdependence index is used to quantify the interdependence between input variables. An adapted genetic algorithm is applied to train the models. The model established by the approach features a unique strong interpretability, which is reflected in three aspects: (1) interpretable evidence acquisition, (2) interpretable inference mechanism, and (3) interpretable parameters determination. The MAKER-based model is shown to be a competitive classifier for the Banana, Haberman’s survival, and Iris data set, and generally performs better than other interpretable classifiers, e.g., complex tree, logistic regression, and naive Bayes.

HACO2 Method for Evolving Hyperbox Classifiers with Ant Colony Optimization

2009 ◽  
Vol 13 (3) ◽  
pp. 338-346
Author(s):  
Guilherme N. Ramos ◽  
◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Data Sets ◽  
Parameter Setting ◽  
Data Set ◽  
2D Data ◽  
Iris Data

A method, called HACO2 (Hyperbox classifier with Ant Colony Optimization - type 2), is proposed for evolving a hyperbox classifier using the ant colony meta-heuristic. It reshapes the hyperboxes in a near-optimal way to better fit the data, improving the accuracy and possibly indicating its most discriminative features. HACO2 is validated using artificial 2D data showing over 90% accuracy. It is also applied to the benchmark iris data set (4 features), providing results with over 93% accuracy, and to the MIS data set (11 features), with almost 85% accuracy. For these sets, the two most discriminative features obtained from the method are used in simplified classifiers which result in accuracies of 100% for the iris and 83% for the MIS data sets. Further modifications (automatic parameter setting), extensions (initialization short comings) and applications are discussed.

scholarly journals Supervised Learning in Multilayer Spiking Neural Networks

2013 ◽  
Vol 25 (2) ◽  
pp. 473-509 ◽  
Author(s):  
Ioana Sporea ◽  
André Grüning
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Neuron Model ◽  
Learning Algorithm ◽  
Spiking Neural Networks ◽  
Reservoir Computing ◽  
Data Set ◽  
Coding Schemes ◽  
Xor Problem ◽  
Iris Data

We introduce a supervised learning algorithm for multilayer spiking neural networks. The algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons firing multiple spikes in artificial neural networks with hidden layers. It can also, in principle, be used with any linearizable neuron model and allows different coding schemes of spike train patterns. The algorithm is applied successfully to classic linearly nonseparable benchmarks such as the XOR problem and the Iris data set, as well as to more complex classification and mapping problems. The algorithm has been successfully tested in the presence of noise, requires smaller networks than reservoir computing, and results in faster convergence than existing algorithms for similar tasks such as SpikeProp.

scholarly journals Visual analysis of self-organizing maps

2011 ◽  
Vol 16 (4) ◽  
pp. 488-504 ◽  
Author(s):  
Pavel Stefanovič ◽  
Olga Kurasova
Keyword(s):  
Data Clustering ◽  
Visual Analysis ◽  
Data Sets ◽  
The European Union ◽  
Self Organizing Maps ◽  
Data Set ◽  
Economic Indices ◽  
Graphical Presentation ◽  
Iris Data ◽  
Self Organizing

In the article, an additional visualization of self-organizing maps (SOM) has been investigated. The main objective of self-organizing maps is data clustering and their graphical presentation. Opportunities of SOM visualization in four systems (NeNet, SOM-Toolbox, Databionic ESOM and Viscovery SOMine) have been investigated. Each system has its additional tools for visualizing SOM. A comparative analysis has been made for two data sets: Fisher’s iris data set and the economic indices of the European Union countries. A new SOM system is also introduced and researched. The system has a specific visualization tool. It is missing in other SOM systems. It helps to see the proportion of neurons, corresponding to the data items, belonging to the different classes, and fallen in the same SOM cell.

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